Machine vision systems combine light detection with image processing and special functions for a variety of applications. So called "smart" image sensors have been developed for gathering and processing images in real time to extract various image features that can be further processed by machine vision algorithms. The initial image processing steps can be important for removing significant loads from a subsequent digital processor, thus allowing production of lower cost machine vision systems. Experimental and commercial sensor systems such as these have been implemented as standard CMOS integrated circuits that incorporate photodiode arrays for image sensing.
Many applications for machine vision systems, especially those used in outdoor environments, require operation over a wide range of lighting conditions. Many of the established low-power analog feature extraction networks, such as for edge or object detection, for example, also require a local voltage signal as an input. Furthermore, some form of intensity compression (such as logarithmic compression, for example) is desirable to provide a uniform threshold of sensitivity for feature extraction from different parts of a scene. Logarithmic compression, however, tends to preclude the use of standard charge-based imaging, which has a linear response to intensity. Alternatively, continuous-time analog circuitry can provide intensity compression with the advantage that clocking overhead (and any associated crosstalk) can be reduced or eliminated.
One of the simplest and most compact photosensor circuits known in the prior art uses two diode-connected FETs in series to convert a photocurrent into a voltage that is logarithmic with respect to light intensity. Although this circuit operates over a wide range of light levels, it suffers from low sensitivity, having only twice the transistor subthreshold slope. Adding more FETs in series has limited effectiveness because threshold voltage becomes a constraint with respect to the direct current operating range of the circuit. One approach to circumvent this problem is temporal adaptation, which does not require an intensity-based referencing or gain/range control signal to be supplied to each pixel. For static scenes, such circuits null out all (or a significant fraction) of the incident light intensity on each pixel, but respond with relatively high sensitivity when the pixel brightness changes. In many of these circuits, the adaptation rate can be adjusted or the pixels can be effectively reset. Although useful in some situations, temporal adaptation is not suitable for applications where fully static scene image data may be needed.
Other prior art analog systems can operate on static images and null out some measure of the average intensity, including a spatially local average, for example. A nonlinear resistive grid can be used to compute a median intensity corresponding to a brightly lit region or a shadow in the scene (directly from a logarithmically compressed voltage signal), for example, and the difference between a pixel's intensity and its "regional average" can be amplified. Such a system requires a differential amplifier in each pixel for its signal to be locally available (and to avoid bussing the signal out of the array for amplification). Another known system uses currents directly from an array of phototransistors to compute a linear average across the whole array. The average current is then subtracted as a current-mode signal from the photocurrent at each pixel to provide an "auto-zeroed" output to a processing network. Although this circuit can be designed to be relatively compact, it does not provide an intensity-compressed voltage-mode output. Because of these limitations of prior art photosensor circuits, there is a need for a compact photo sensor amplifying circuit having high sensitivity and wide dynamic range that automatically adapts to overall light intensity.